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Machines MDPI

Machines (ISSN 2075-1702, IF: 2.1) - Open Access Journal on machinery and engineering published monthly online by MDPI.

22/08/2025

🔔 State of for Lithium-Ion Based on Fractional-Order Kreisselmeier-Type

✍️ Tomoki Murakami and Hiromitsu Ohmori from Keio University

🔗 https://www.mdpi.com/2075-1702/12/10/738

22/08/2025

🔍 - of an for Multi-Directional

✍️ Fabio Carapellese, Viola De Clerck, Sergej Antonello Sirigu, Giuseppe Giorgi, Mauro Bonfanti, Nicolás Faedo and Ermanno Giorcelli from Politecnico di Torino

🔗 https://www.mdpi.com/2075-1702/12/10/736

21/08/2025

💡 of a Device Based on a

✍️ Francisco J. Campa and Daniel Díaz-Caneja

🔗 https://www.mdpi.com/2075-1702/12/10/735

21/08/2025

🙌 Welcome Dr. Nicola Ivan Giannoccaro – Committee Member of !

We are excited to welcome Dr. Nicola Ivan Giannoccaro (University of Salento) as the Committee Member for the 3rd International Electronic Conference on Machines and Applications (IECMA2026)!

📅 Save the Date: 12-14 May 2026, Online
🔗 Submit yours by 9 January 2026: https://sciforum.net/user/submission/create/1402
🔗 Free registration: https://sciforum.net/event/IECMA2026?section=
🔗 More about the event: https://sciforum.net/event/IECMA2026
📩 Any Questions? Reach us at [email protected]

20/08/2025

🏆 Editor’s Choice

"The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation "

📌 This study introduces a novel gear wear prediction model specifically designed for standard involute spur gears operating under dry friction conditions. The model incorporates the effects of the initial tooth surface profile and temperature variations on the tooth profile. It considers the elastic interactions between adjacent gear teeth, as well as the variations in wear along the width of the tooth. By establishing a coupled relationship between the dynamic model, temperature, and tooth surface morphology, the model achieves a high degree of correlation between gear wear prediction and tooth surface morphology, temperature, and vibration. The reliability of the wear prediction model was validated through experimental wear tests.

✍️ Jingqi Zhang et al.
🔗 Read here: https://www.mdpi.com/2075-1702/12/10/734

20/08/2025

⚙️ of in

✍️ Zhiqiang Guo, Junlin Luo and Yuwei Liu

🔗 https://www.mdpi.com/2075-1702/12/10/733

20/08/2025

🚗 of for the

✍️ Pedro M. P. Curralo, Raul D. S. G. Campilho, Joaquim A. P. Pereira and Francisco J. G. Silva from Polytechnic of Porto

🔗 https://www.mdpi.com/2075-1702/12/10/731

19/08/2025

🔔 Read Highly Cited Papers in 2024 - Part One

1. Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning
Hussain, M.; Zhang, T.; Chaudhry, M.; Jamil, I.; Kausar, S.; Hussain, I.
https://www.mdpi.com/2075-1702/12/1/42

2. A Survey on Path Planning for Autonomous Ground Vehicles in Unstructured Environments
Wang, N.; Li, X.; Zhang, K.; Wang, J.; Xie, D.
https://www.mdpi.com/2075-1702/12/1/31

3. Deep Learning-Enhanced Small-Sample Bearing Fault Analysis Using Q-Transform and HOG Image Features in a GRU-XAI Framework
Dave, V.; Borade, H.; Agrawal, H.; Purohit, A.; Padia, N.; Vakharia, V.
https://www.mdpi.com/2075-1702/12/6/373

4. The Role of Ergonomic and Human Factors in Sustainable Manufacturing: A Review
Hasanain, B.
https://www.mdpi.com/2075-1702/12/3/159

5. Localization and Mapping for Self-Driving Vehicles: A Survey
Charroud, A.; El Moutaouakil, K.; Palade, V.; Yahyaouy, A.; Onyekpe, U.; Eyo, E.U.
https://www.mdpi.com/2075-1702/12/2/118

6. Emotional Intelligence for the Decision-Making Process of Trajectories in Collaborative Robotics
Antonelli, M.G.; Beomonte Zobel, P.; Manes, C.; Mattei, E.; Stampone, N.
https://www.mdpi.com/2075-1702/12/2/113

19/08/2025

📣 Conference Announcement

🌟 Structural Health Monitoring for a Sustainable Future – Meet the MDPI Team at

📅 Join us at the 15th International Workshop on Structural Health Monitoring (IWSHM), taking place 9–11 September 2025 at Stanford University, California!

This year’s theme, “SHM: Ensuring Mobility and Autonomy with Sustainability,” highlights the vital role of structural health monitoring in enabling sustainable systems through condition-based maintenance and performance-driven design.

📍 Visit us at our booth to learn more about MDPI’s related open access journals, including:

Buildings MDPI, Sensors MDPI, Biomimetics MDPI, CivilEng MDPI, Vehicles MDPI, Infrastructures MDPI, , Technologies MDPI, Wind MDPI, Aerospace MDPI, NDT MDPI, Vibration, JETA MDPI, and Applied Mechanics MDPI.

Our team will be on-site to discuss publishing opportunities, related initiatives, and how MDPI can support your research in SHM, sustainability, and beyond. We look forward to connecting with you!

🔗 More info about the event and how to register: https://www.mdpi.com/journal/machines/announcements/12920

19/08/2025

🙌 Welcome Prof. Dr. Jan Awrejcewicz – Committee Member of !

We are excited to welcome Prof. Dr. Jan Awrejcewicz (Lodz University of Technology) as the Committee Member for the 3rd International Electronic Conference on Machines and Applications (IECMA2026)!

📅 Save the Date: 12-14 May 2026, Online
🔗 Submit yours by 9 January 2026: https://sciforum.net/user/submission/create/1402
🔗 Free registration: https://sciforum.net/event/IECMA2026?section=
🔗 More about the event: https://sciforum.net/event/IECMA2026
📩 Any Questions? Reach us at [email protected]

18/08/2025

💡 and Study of of

✍️ Zemin Zhao, Shuangshuang Zhou, Qiang Liu, Long Zhang, Bin Shen and Jiaming Han

🔗 https://www.mdpi.com/2075-1702/12/10/732

18/08/2025

🏆 Editor’s Choice

"Thermal Error Transfer Prediction Modeling of Machine Tool Spindle with Self-Attention Mechanism-Based Feature Fusion"

In this paper, a thermal error model based on transfer temperature feature fusion is proposed. Temperature features were fused by self-attentive weights and used as inputs in the model. The fused features were subjected to domain adaptation based on an improved adaptive matrix of direct normalization. An EasyTL algorithm implements a nonparametric transfer model through in-domain pairs and in-domain programming learning to shorten the model training time and ensure the model prediction accuracy.

📌 Why it matters:
✅ Firstly, sensitive temperature features were fused through a self-attention mechanism.
✅ Secondly, an improved adaptive matrix method based on direct normalization was proposed to narrow the distribution gap between the source and target domains and improve knowledge transfer.
✅ Thirdly, with the advantages of EasyTL’s nonparametric and efficient learning, intra-domain alignment was performed to align the different feature distributions in the source and target domains.

✍️ Yue Zheng et al.

🔗 Read here: https://www.mdpi.com/2075-1702/12/10/728

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